Report Sleep Deprivation

Importing data

countMatrix <- ReadDataFrameFromTsv(file.name.path="../data/refSEQ_countMatrix.txt")

## ../data/refSEQ_countMatrix.txt read from disk!

# head(countMatrix)

designMatrix <- ReadDataFrameFromTsv(file.name.path="../design/all_samples_short_names_noRS2HC7.tsv")

## ../design/all_samples_short_names_noRS2HC7.tsv read from disk!

# head(designMatrix)

rownames <- as.character(rownames(countMatrix))

rownames <- rownames[order(rownames)]
rownames.map <- convertGenesViaBiomart(specie="mm10", filter="entrezgene",
                    filter.values=rownames, attrs=c("external_gene_name",
                                "mgi_symbol", "entrezgene"))

noNaCountMatrix <- attachGeneColumnToDf(mainDf=countMatrix,
                                genesMap=rownames.map,
                                rowNamesIdentifier="entrezgene",
                                mapFromIdentifier="entrezgene",
                                mapToIdentifier="external_gene_name")


filteredCountsProp <- filterLowCounts(counts.dataframe=noNaCountMatrix, 
                                    is.normalized=FALSE,
                                    design.dataframe=designMatrix,
                                    cond.col.name="gcondition",
                                    method.type="Proportion")

## features dimensions before normalization: 27179

## Filtering out low count features...
## 14454 features are to be kept for differential expression analysis with filtering method 3

Plot PCA of log unnormalized data

PlotPCAPlotlyFunction(counts.data.frame=log1p(filteredCountsProp), design.matrix=designMatrix, shapeColname="condition", colorColname="genotype", xPCA="PC1", yPCA="PC2", plotly.flag=TRUE, show.plot.flag=TRUE, prefix.plot="Prop-Un-Norm")

## [1] TRUE

# pc1_2 <- PlotPCAPlotlyFunction(counts.data.frame=log1p(filteredCountsProp), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC1", yPCA="PC2", plotly.flag=TRUE, show.plot.flag=FALSE, prefix.plot="Prop-Un-Norm")
# pc2_3 <- PlotPCAPlotlyFunction(counts.data.frame=log1p(filteredCountsProp), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC2", yPCA="PC3", plotly.flag=TRUE, show.plot.flag=FALSE, prefix.plot="Prop-Un-Norm")
# pc1_3 <- PlotPCAPlotlyFunction(counts.data.frame=log1p(filteredCountsProp), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC1", yPCA="PC3", plotly.flag=TRUE, show.plot.flag=FALSE, prefix.plot="Prop-Un-Norm")
# plotly::subplot(pc1_2, pc2_3, pc1_3, nrows=2, margin = 0.1, titleX=TRUE, titleY=TRUE)

control genes

Loading Negative Control Genes to normalize data

library(readxl)

sd.ctrls <- read_excel(path="../data/controls/Additional File 4 full list of BMC genomics SD&RS2.xlsx", sheet=1)
sd.ctrls <- sd.ctrls[order(sd.ctrls$adj.P.Val),]

sd.neg.ctrls <- sd.ctrls[sd.ctrls$adj.P.Val > 0.9, ]

sd.neg.ctrls <- sd.neg.ctrls$`MGI Symbol`
sd.neg.ctrls <- sd.neg.ctrls[-which(is.na(sd.neg.ctrls))]

int.neg.ctrls <- sd.neg.ctrls
neg.map <- convertGenesViaBiomart(specie="mm10", filter="mgi_symbol",
                    filter.values=int.neg.ctrls, c("external_gene_name",
                                "mgi_symbol", "entrezgene"))

neg.map.nna <- neg.map[-which(is.na(neg.map$entrezgene)),]

neg.ctrls.entrez <- as.character(neg.map.nna$entrezgene)

ind.ctrls <- which(rownames(filteredCountsProp) %in% neg.ctrls.entrez)
counts.neg.ctrls <- filteredCountsProp[ind.ctrls,]
# pc1_2 <- PlotPCAPlotlyFunction(counts.data.frame=log1p(counts.neg.ctrls), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC1", yPCA="PC2", plotly.flag=TRUE, show.plot.flag=FALSE, prefix.plot="Neg Ctrls not Norm")
# pc2_3 <- PlotPCAPlotlyFunction(counts.data.frame=log1p(counts.neg.ctrls), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC2", yPCA="PC3", plotly.flag=TRUE, show.plot.flag=FALSE, prefix.plot="Neg Ctrls not Norm")
# pc1_3 <- PlotPCAPlotlyFunction(counts.data.frame=log1p(counts.neg.ctrls), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC1", yPCA="PC3", plotly.flag=TRUE, show.plot.flag=FALSE, prefix.plot="Neg Ctrls not Norm")
# plotly::subplot(pc1_2, pc2_3, pc1_3, nrows=2, margin = 0.1, titleX=TRUE, titleY=TRUE)

positive control genes

Positive Control Genes

## sleep deprivation
sd.lit.pos.ctrls <- read_excel("../data/controls/SD_RS_PosControls_final.xlsx", 
                        sheet=1)
colnames(sd.lit.pos.ctrls) <- sd.lit.pos.ctrls[1,]
sd.lit.pos.ctrls <- sd.lit.pos.ctrls[-1,]


sd.est.pos.ctrls <- read_excel("../data/controls/SD_RS_PosControls_final.xlsx", 
                        sheet=3)

sd.pos.ctrls <- cbind(sd.est.pos.ctrls$`MGI Symbol`, "est")
sd.pos.ctrls <- rbind(sd.pos.ctrls, cbind(sd.lit.pos.ctrls$Gene, "lit"))

sd.pos.ctrls <- sd.pos.ctrls[-which(duplicated(sd.pos.ctrls[,1])),]
sd.pos.ctrls <- sd.pos.ctrls[-which(is.na(sd.pos.ctrls[,1])),]

Normalizations

TMM Normalization

normPropCountsUqua <- NormalizeData(data.to.normalize=filteredCountsProp, 
                                    norm.type="tmm", 
                                    design.matrix=designMatrix)

PlotPCAPlotlyFunction(counts.data.frame=log1p(normPropCountsUqua), design.matrix=designMatrix, shapeColname="condition", colorColname="genotype", xPCA="PC1", yPCA="PC2", plotly.flag=TRUE, show.plot.flag=TRUE, prefix.plot="TMM-Norm")

## [1] TRUE

# pc1_2 <- PlotPCAPlotlyFunction(counts.data.frame=log1p(normPropCountsUqua), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC1", yPCA="PC2", plotly.flag=TRUE, show.plot.flag=FALSE, prefix.plot="TMM-Norm")
# pc2_3 <- PlotPCAPlotlyFunction(counts.data.frame=log1p(normPropCountsUqua), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC2", yPCA="PC3", plotly.flag=TRUE, show.plot.flag=FALSE, prefix.plot="TMM-Norm")
# pc1_3 <- PlotPCAPlotlyFunction(counts.data.frame=log1p(normPropCountsUqua), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC1", yPCA="PC3", plotly.flag=TRUE, show.plot.flag=FALSE, prefix.plot="TMM-Norm")
# plotly::subplot(pc1_2, pc2_3, pc1_3, nrows=2, margin = 0.1, titleX=TRUE, titleY=TRUE)


pal <- RColorBrewer::brewer.pal(9, "Set1")
plotRLE(as.matrix(normPropCountsUqua), outline=FALSE, col=pal[designMatrix$gcondition])

TMM + RUVs Normalization

K=5

library(RUVSeq)
neg.ctrl.list <- rownames(counts.neg.ctrls)
groups <- makeGroups(designMatrix$gcondition)
ruvedSExprData <- RUVs(as.matrix(round(normPropCountsUqua)), cIdx=neg.ctrl.list,
                       scIdx=groups, k=5)

normExprData <- ruvedSExprData$normalizedCounts

PlotPCAPlotlyFunction(counts.data.frame=log1p(normExprData), design.matrix=designMatrix, shapeColname="condition", colorColname="genotype", xPCA="PC1", yPCA="PC2", plotly.flag=TRUE, show.plot.flag=TRUE, prefix.plot="TMM+RUV-Norm")

## [1] TRUE

# pc1_2 <- PlotPCAPlotlyFunction(counts.data.frame=log1p(normExprData), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC1", yPCA="PC2", plotly.flag=TRUE, show.plot.flag=FALSE, prefix.plot="TMM+RUV-Norm")
# pc2_3 <- PlotPCAPlotlyFunction(counts.data.frame=log1p(normExprData), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC2", yPCA="PC3", plotly.flag=TRUE, show.plot.flag=FALSE, prefix.plot="TMM+RUV-Norm")
# pc1_3 <- PlotPCAPlotlyFunction(counts.data.frame=log1p(normExprData), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC1", yPCA="PC3", plotly.flag=TRUE, show.plot.flag=FALSE, prefix.plot="TMM+RUV-Norm")
# plotly::subplot(pc1_2, pc2_3, pc1_3, nrows=2, margin = 0.1, titleX=TRUE, titleY=TRUE)

pal <- RColorBrewer::brewer.pal(9, "Set1")
plotRLE(normExprData, outline=FALSE, col=pal[designMatrix$gcondition])

edgeR DE Analysis

### edgering
padj.thr <- 0.05
desMat <- cbind(designMatrix, ruvedSExprData$W)
colnames(desMat) <- c(colnames(designMatrix), colnames(ruvedSExprData$W))

cc <- c("WTSD5 - WTHC5", "KOHC5 - WTHC5",
        "KOSD5 - WTSD5", "KOSD5 - KOHC5")

rescList1 <- applyEdgeR(counts=filteredCountsProp, design.matrix=desMat,
                        factors.column="gcondition", 
                        weight.columns=c("W_1", "W_2", "W_3", "W_4", "W_5"),
                        contrasts=cc, useIntercept=FALSE, p.threshold=1,
                        is.normalized=FALSE, verbose=TRUE)

WTSD5 - WTHC5

pvalue histogram

PlotHistPvalPlot(de.results=rescList1[[1]], design.matrix=desMat, 
                show.plot.flag=TRUE, plotly.flag=TRUE, 
                prefix.plot=names(rescList1)[1])

volcano plot

## mapping ensembl gene id using biomart
# sd.pos.ctrls <- sd.pos.ctrls$`MGI Symbol`
# sd.pos.ctrls <- sd.pos.ctrls[-which(is.na(sd.pos.ctrls))]
# length(sd.pos.ctrls)

res.o.map <- convertGenesViaBiomart(specie="mm10", filter="entrezgene",
                            filter.values=rownames(rescList1[[1]]),
                            c("external_gene_name", "mgi_symbol", "entrezgene"))

res.o <- attachGeneColumnToDf(mainDf=rescList1[[1]],
                                genesMap=res.o.map,
                                rowNamesIdentifier="entrezgene",
                                mapFromIdentifier="entrezgene",
                                mapToIdentifier="external_gene_name")

WriteDataFrameAsTsv(data.frame.to.save=res.o, 
                    file.name.path=paste0(names(rescList1)[1], "_edgeR"))

vp <- luciaVolcanoPlot(res.o, sd.pos.ctrls, prefix=names(rescList1)[1], 
                    threshold=padj.thr)
ggplotly(vp)

# vp <- PlotVolcanoPlot(de.results=res.o, counts.dataframe=normExprData, 
#                 design.matrix=desMat,
#                 show.plot.flag=FALSE, plotly.flag=FALSE, save.plot=FALSE, 
#                 prefix.plot=names(rescList1)[1], threshold=padj.thr)#, 
#                 #positive.ctrls.list=sd.pos.ctrls)

de <- sum(res.o$FDR < padj.thr)
nde <- sum(res.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[1]
ddetable <- detable

tot.ctrls <- dim(sd.pos.ctrls)[1]
idx.pc <- which(tolower(res.o$gene) %in% tolower(sd.pos.ctrls[,1]))
tot.pc.de <- sum(res.o$FDR[idx.pc] < padj.thr)
tot.pc.nde <- length(idx.pc) - tot.pc.de
pos.df <- cbind(tot.ctrls, tot.pc.de, tot.pc.nde)
colnames(pos.df) <- c("total_p.ctrl", "p.ctrl_de_mapped", 
                    "p.ctrl_notde_mapped")
rownames(pos.df) <- names(rescList1)[1]
# PlotMAPlotCounts(de.results=res.o, counts.dataframe=normExprData, design.matrix=desMat,
#                  show.plot.flag=TRUE, plotly.flag=TRUE, save.plot=FALSE, prefix.plot=names(rescList1)[1], threshold=0.05)

KOHC5 - WTHC5

pvalue histogram

PlotHistPvalPlot(de.results=rescList1[[2]], design.matrix=desMat, 
                show.plot.flag=TRUE, plotly.flag=TRUE, 
                prefix.plot=names(rescList1)[2])

volcano plot

rs2.o.map <- convertGenesViaBiomart(specie="mm10", filter="entrezgene",
                            filter.values=rownames(rescList1[[2]]),
                            c("external_gene_name", "mgi_symbol", "entrezgene"))

res.rs2.o <- attachGeneColumnToDf(mainDf=rescList1[[2]],
                                genesMap=rs2.o.map,
                                rowNamesIdentifier="entrezgene",
                                mapFromIdentifier="entrezgene",
                                mapToIdentifier="external_gene_name")

WriteDataFrameAsTsv(data.frame.to.save=res.rs2.o, 
                    file.name.path=paste0(names(rescList1)[2], "_edgeR"))

vp <- luciaVolcanoPlot(res.rs2.o, positive.controls.df=NULL, 
                       prefix=names(rescList1)[2], 
                       threshold=padj.thr)
ggplotly(vp)

# PlotVolcanoPlot(de.results=res.rs2.o, counts.dataframe=normExprData, 
#                 design.matrix=desMat,
#                 show.plot.flag=TRUE, plotly.flag=TRUE, save.plot=FALSE, 
#                 prefix.plot=names(rescList1)[2], threshold=padj.thr, 
#                 positive.ctrls.list=rs2.pos.ctrls)
de <- sum(res.rs2.o$FDR < padj.thr)
nde <- sum(res.rs2.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[2]
ddetable <- rbind(ddetable, detable)

# 
# tot.ctrls <- dim(rs.pos.ctrls)[1]
# idx.pc <- which(tolower(res.o$gene) %in% tolower(rs.pos.ctrls[,1]))
# tot.pc.de <- sum(res.o$FDR[idx.pc] < padj.thr)
# tot.pc.nde <- length(idx.pc) - tot.pc.de
# pos.dff <- cbind(tot.ctrls, tot.pc.de, tot.pc.nde)
# rownames(pos.dff) <- names(rescList1)[2]
# pos.df <- rbind(pos.df, pos.dff)
# # colnames(pos.df) <- c("total p.ctrl", "p.ctrl de mapped", 
# #                     "p.ctrl not de mapped")

# PlotMAPlotCounts(de.results=res.o, counts.dataframe=normExprData, design.matrix=desMat,
                 # show.plot.flag=TRUE, plotly.flag=TRUE, save.plot=FALSE, prefix.plot=names(rescList1)[2], threshold=0.05)

KOSD5 - WTSD5

pvalue histogram

PlotHistPvalPlot(de.results=rescList1[[3]], design.matrix=desMat, 
                show.plot.flag=TRUE, plotly.flag=TRUE, 
                prefix.plot=names(rescList1)[3])

volcano plot

## mapping ensembl gene id using biomart

res.o.map <- convertGenesViaBiomart(specie="mm10", filter="entrezgene",
                            filter.values=rownames(rescList1[[3]]),
                            c("external_gene_name", "mgi_symbol", "entrezgene"))

res.o <- attachGeneColumnToDf(mainDf=rescList1[[3]],
                                genesMap=res.o.map,
                                rowNamesIdentifier="entrezgene",
                                mapFromIdentifier="entrezgene",
                                mapToIdentifier="external_gene_name")

WriteDataFrameAsTsv(data.frame.to.save=res.o, 
                    file.name.path=paste0(names(rescList1)[3], "_edgeR"))

vp <- luciaVolcanoPlot(res.o, positive.controls.df=sd.pos.ctrls, 
                        prefix=names(rescList1)[3], threshold=padj.thr)
ggplotly(vp)

de <- sum(res.o$FDR < padj.thr)
nde <- sum(res.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[3]
ddetable <- rbind(ddetable, detable)


tot.ctrls <- dim(sd.pos.ctrls)[1]
idx.pc <- which(tolower(res.o$gene) %in% tolower(sd.pos.ctrls[,1]))
tot.pc.de <- sum(res.o$FDR[idx.pc] < padj.thr)
tot.pc.nde <- length(idx.pc) - tot.pc.de
pos.dff <- cbind(tot.ctrls, tot.pc.de, tot.pc.nde)
rownames(pos.dff) <- names(rescList1)[2]
pos.df <- rbind(pos.df, pos.dff)
# PlotMAPlotCounts(de.results=res.o, counts.dataframe=normExprData, design.matrix=desMat,
#                  show.plot.flag=TRUE, plotly.flag=TRUE, save.plot=FALSE, prefix.plot=names(rescList1)[1], threshold=0.05)

KOSD5 - WTHC5

pvalue histogram

PlotHistPvalPlot(de.results=rescList1[[4]], design.matrix=desMat, 
                show.plot.flag=TRUE, plotly.flag=TRUE, 
                prefix.plot=names(rescList1)[4])

volcano plot

## mapping ensembl gene id using biomart

res.o.map <- convertGenesViaBiomart(specie="mm10", filter="entrezgene",
                            filter.values=rownames(rescList1[[4]]),
                            c("external_gene_name", "mgi_symbol", "entrezgene"))


WriteDataFrameAsTsv(data.frame.to.save=rescList1[[4]], 
                    file.name.path=paste0(names(rescList1)[4], "_edgeR"))

res.o <- attachGeneColumnToDf(mainDf=rescList1[[4]],
                                genesMap=res.o.map,
                                rowNamesIdentifier="entrezgene",
                                mapFromIdentifier="entrezgene",
                                mapToIdentifier="external_gene_name")

WriteDataFrameAsTsv(data.frame.to.save=res.o, 
                    file.name.path=paste0(names(rescList1)[4], "_edgeR"))


vp <- luciaVolcanoPlot(res.o, positive.controls.df=sd.pos.ctrls, 
                        prefix=names(rescList1)[4], threshold=padj.thr)
ggplotly(vp)

# 
# PlotVolcanoPlot(de.results=res.o, counts.dataframe=normExprData, 
#                 design.matrix=desMat,
#                 show.plot.flag=TRUE, plotly.flag=TRUE, save.plot=FALSE, 
#                 prefix.plot=names(rescList1)[4], threshold=padj.thr, 
#                 positive.ctrls.list=NULL)

de <- sum(res.o$FDR < padj.thr)
nde <- sum(res.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[4]
ddetable <- rbind(ddetable, detable)


tot.ctrls <- dim(sd.pos.ctrls)[1]
idx.pc <- which(tolower(res.o$gene) %in% tolower(sd.pos.ctrls[,1]))
tot.pc.de <- sum(res.o$FDR[idx.pc] < padj.thr)
tot.pc.nde <- length(idx.pc) - tot.pc.de
pos.dff <- cbind(tot.ctrls, tot.pc.de, tot.pc.nde)
rownames(pos.dff) <- names(rescList1)[2]
pos.df <- rbind(pos.df, pos.dff)
# PlotMAPlotCounts(de.results=res.o, counts.dataframe=normExprData, design.matrix=desMat,
#                  show.plot.flag=TRUE, plotly.flag=TRUE, save.plot=FALSE, prefix.plot=names(rescList1)[1], threshold=0.05)

DE TABLE + Positive Controls table

ddetable

##                 de   nde
## WTSD5 - WTHC5 5603  8851
## KOHC5 - WTHC5   39 14415
## KOSD5 - WTSD5   80 14374
## KOSD5 - KOHC5 5714  8740

pos.df

##               total_p.ctrl p.ctrl_de_mapped p.ctrl_notde_mapped
## WTSD5 - WTHC5          579              439                  98
## KOHC5 - WTHC5          579                4                 533
## KOHC5 - WTHC5          579              438                  99